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GPGPU Final Project Proposal

GPGPU Final Project Proposal. r00922016 朱冠宇 r00922042 李哲君 2012/04/17. Topic. Hierarchical K-means. Motivation. K-means can be used for lots of applications Market research Social network analysis Recommendation system Image retrieval system. Motivation - speed.

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GPGPU Final Project Proposal

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  1. GPGPU Final Project Proposal r00922016 朱冠宇 r00922042 李哲君 2012/04/17

  2. Topic Hierarchical K-means

  3. Motivation K-means can be used for lots of applications • Market research • Social network analysis • Recommendation system • Image retrieval system

  4. Motivation - speed Data: N * d, find K centroids Find the nearest centroid: sub : N * d * k, mul : N * d * k, add : N * d min : O(k) Calculate new centroid: add : N * d, div : K * d Therefore, Hierarchical K-Means (HKM)shows up , but it is still slow for high dimension and large data.

  5. Experiment on HKM cpu version Holiday dataset 1491 pictures 46518801 vectors1000000 centroids Time : 626m55.739s

  6. Schedule & Scope 4/16~4/20K-means(GPU), HKM(CPU) code survey 4/23~5/04HKM GPU basic version 5/07~5/18Code speed up 5/21~6/01Clustering visualization or some app. 6/01~6/12buffer and write poster

  7. Expected Outcome big dimension (>128 for sift) HKM CUDA lib HKM(GPU) v.s. HKM(CPU) report clustering visualization (optional)

  8. Thanks for your attention.

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